Choosing a Statistical Test
There are many statistical tests like t-test, z-test, or ANOVA test. The choice of a test depends on certain factors. I have described those factors in the paragraph above. The choice of common statistical tests depend on the type of data distribution. If the data is normally distributed, the analysts use parametric tests. The parametric tests are also the most commonly used tests in statistical analysis. There are further three types of parametric tests which are as follows;T-test
The first common statistical test is the T-test. In the T-test, the researcher compares the mean of the two variables. The analysts use this test when population parameters are unknown. Now, what are the population parameters? The population parameters are the mean and standard deviation. There are three further types of T-tests. The description of those tests is as follows;Paired T-test
The analysts use this test to compare two variables from the same population. It includes pre and post-scores of the population. For example, in a training program, the analysts give scores to the participants. The researcher will use paired t-test to analyze the scores before and after the program.Independent T-test
This test determines the statistical difference between two unrelated groups. It is also called a two-sample t-test and a students’ t-test. The typical example of this test is comparing boys and girls in a population.One sample T-test
Among these types of common statistical tests, the researcher compares the mean of a group with the given mean. One typical example of this is to check the increase and decrease in the sales with an average sale already given.ANOVA test
Analysis of variance (ANOVA) is a very common statistical test. The analysts use this to check the difference between the means of two or more groups. This test checks the effect of different factors on the means of different samples. The number of samples in this test is always more than two. Therefore, we can’t use a t-test instead of an ANOVA.The ANOVA tests the null hypothesis that all means of the samples are the same. The research question in the ANOVA is very clear. It is “Is there a difference between the means of our sample groups?” There are assumptions that you need to make before applying the ANOVA test. These assumptions are as follows;
- The variance of all the population samples is the same
- The population sample of each group is normally distributed
- The researcher has made observations randomly
- Every sample is independent of the other
Correlation and Regression Test
Among most common statistical tests is the regression and correlation test. It relates to the fundamental goal of statistics, i.e., to identify relationships. In the correlation test, the two variables are always symmetric. It doesn’t matter which one is dependent and which is independent. But it does matter in regression tests. In regression, one variable is designated as dependent and the other as independent. The researcher sometimes calls the independent variable an explanatory variable. But in a multiple regression test, there will be more than one independent variable. Regression is also the most common statistical test that analysts use.Also Read This: Things You Should Know About Pathway Course
In regression, there will be a correlation coefficient. The correlation coefficient measures the level of relationship between the two variables. Pearson coefficient is one of the most common correlation coefficients. The regression analysis provides you with an equation. The researchers use that equation to make predictions about the data. There is no minimum sample size limitation for regression analysis. You just need to have a dependent and independent variable.
In regression, there will be a correlation coefficient. The correlation coefficient measures the level of relationship between the two variables. Pearson coefficient is one of the most common correlation coefficients. The regression analysis provides you with an equation. The researchers use that equation to make predictions about the data. There is no minimum sample size limitation for regression analysis. You just need to have a dependent and independent variable.